https://github.com/yonv1943/unsupervised-segmentation
A high performance impermentation of Unsupervised Image Segmentation by Backpropagation - Asako Kanezaki
https://github.com/yonv1943/unsupervised-segmentation
unsupervised
Last synced: 9 months ago
JSON representation
A high performance impermentation of Unsupervised Image Segmentation by Backpropagation - Asako Kanezaki
- Host: GitHub
- URL: https://github.com/yonv1943/unsupervised-segmentation
- Owner: Yonv1943
- Created: 2019-06-19T07:16:13.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-06-19T09:36:03.000Z (about 7 years ago)
- Last Synced: 2025-03-30T05:11:21.740Z (over 1 year ago)
- Topics: unsupervised
- Language: Python
- Homepage:
- Size: 4.73 MB
- Stars: 264
- Watchers: 6
- Forks: 54
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Unsupervised-Segmentation
### An implementation of **Unsupervised Image Segmentation by Backpropagation - Asako Kanezaki 金崎朝子** （東京大学）ICASSP. 2018.
### **Faster and more elegant than origin version. Speed up, 30s(origin) --> 5s(modify)**
Paper: https://kanezaki.github.io/pytorch-unsupervised-segmentation/ICASSP2018_kanezaki.pdf
Original version Github: https://github.com/kanezaki/pytorch-unsupervised-segmentation
An Interpretation of this algorithm: https://zhuanlan.zhihu.com/p/68528056 (Warning: Simplified Chinese)
## Requement
Necessary: Python 3, Torch 0.4
Unnecessary: skimage, opencv-python(cv2)
## Getting Started
Try the high performance code written by me.
```
python3 demo_modify.py
class Args(object): # You can change the input_image_path ↓
input_image_path = 'image/woof.jpg' # image/coral.jpg image/tiger.jpg
```
Or you want to try the code written by the original author.
```
python3 demo_origin.py
python3 demo_origin.py --input image/woof.jpg
```
Run this demo, and **press WASDQE on the keyboard** to adjust the parameters.
The image show in the GUI, and the parameters show in terminal in real time.
You could choose **Algorithm felz** or **Algorithm slic** by commenting the code.
* W,S --> parameter 1
* A,D --> parameter 2
* Q,E --> parameter 3
```
python3 demo_pre_seg__felz_slic.py
```
## Preview
The iterative process: Save the result when the iter_number == 1,2,4,8,16,32,64,128.
The different result of **Algorithm felz** or **Algorithm slic** with different parameters.
The left picture: compactness = 10000
The right picture: compactness = 1000
The left picture: **Algorithm slic**
The right picture: **Algorithm felz**
## Translate 翻译
#### If you can understand English, then I know you can understand this line of words (and you see this line on GitHub.)
#### 如果你可以看得懂中文，那么我对这个算法的分析写在知乎上了（或者你就是从知乎过来的）
#### An implementation of **Unsupervised Image Segmentation by Backpropagation**
#### 无监督图片语义分割，复现并魔改Github上的项目 https://zhuanlan.zhihu.com/p/68528056
#### In my opinion, this algorithm is well suited for unsupervised segmentation of satellite images, because satellite images have no directionality. It is suitable for this algorithm with a priori assumption. (Priori Assumptions: In general, the regions with the same semantic information on the satellite images tend to occurs in a continuous area)
#### 这个算法很适合做 卫星图片的无监督语义分割任务，因为卫星地图没有方向性，并且地图上带有相同语义信息的区域往往是出现在一起的（符合先验假设）。很适合这种带有这种的先验假设算法。